Load required packages into R session.
url <- "https://raw.githubusercontent.com/hickslab/ProgenesisLFQ/master/"
source_url(paste0(url, "R/ProgenesisLFQ_Global.R"))## SHA-1 hash of file is 46d72ef9fbf7843a06a8c125d64eaf3c02bfa2e5
Load Protein Measurements spreadsheet exported from Progenesis.
protm <- "data/20180502_WOS52_Cr_UPS_protm.csv" %>%
paste0(url, .) %>%
read_csv(., skip = 2, col_types = cols())Separte leading protein accession from group members.
protm <- protm %>%
separate(Accession,
into = c("Accession", "Group members"),
sep = ";",
extra = "merge",
fill = "right")
protmDefine column indeces for the normalized abundance values.
## [1] "Accession" "Group members"
## [3] "Peptide count" "Unique peptides"
## [5] "Confidence score" "Anova (p)"
## [7] "Max fold change" "Highest mean condition"
## [9] "Lowest mean condition" "Description"
## [11] "20141222_WOS521" "20141222_WOS526"
## [13] "20141222_WOS5211" "20141222_WOS5216"
## [15] "20141222_WOS522" "20141222_WOS527"
## [17] "20141222_WOS5212" "20141222_WOS5217"
## [19] "20141222_WOS523" "20141222_WOS528"
## [21] "20141222_WOS5213" "20141222_WOS5218"
## [23] "20141222_WOS521_1" "20141222_WOS526_1"
## [25] "20141222_WOS5211_1" "20141222_WOS5216_1"
## [27] "20141222_WOS522_1" "20141222_WOS527_1"
## [29] "20141222_WOS5212_1" "20141222_WOS5217_1"
## [31] "20141222_WOS523_1" "20141222_WOS528_1"
## [33] "20141222_WOS5213_1" "20141222_WOS5218_1"
## [1] 11 12 13 14 15 16 17 18 19 20 21 22
Filter to remove proteins from the contaminant database.
Filter to remove proteins if not enough peptide evidence.
Select the identifier and abundance columns.
data <- protm %>%
filter(Description != "cRAP") %>%
filter(`Peptide count` >= 2 & `Unique peptides` >= 1) %>%
select(Accession, samples) %>%
data.frame()
dataExample plot construction.
plot_jitter <- function(df){
temp.df <- df %>%
gather(sample, abundance, -1)
temp.df %>%
ggplot(., aes(x = sample, y = abundance, color = sample)) +
geom_jitter(alpha = 0.5) +
geom_boxplot(color = "black",
fill = NA,
outlier.shape = NA,
size = 1.5) +
guides(color = FALSE, fill = FALSE) +
theme_classic(base_size = 32) +
coord_flip()
}
data %>% plot_jitter()Peptide-Level Quantification
During raw MS file processing, Progenesis subdivided each LC-MS run into peak features, which are MS1 precursor ions with a defined isotopic cluster with characteristic retention time and monoisotopic mass. The same peak feature coordinates are assigned for every LC-MS run and the summed intensity (abundance) from each is recorded.
MS2 spectra from data-dependent acquisition (DDA) contain the associated MS1 precursor ion mass and retention time, allowing them to be mapped to peak features.
The Peptide Measurements export from Progenesis contains the normalized abundances, raw abundances, and spectral counts for each identified peptide that was mapped to a MS1 peak feature in each raw file.
Load required packages into R session.
url <- "https://raw.githubusercontent.com/hickslab/ProgenesisLFQ/master/"
source_url(paste0(url, "R/ProgenesisLFQ_Peptide.R"))## SHA-1 hash of file is 4696e657853f00386fbdb70cfb61001bd220c3fa
Load Peptide Measurements spreadsheet exported from Progenesis.
pepm <- "data/20180502_WOS52_Cr_UPS_pepm.csv" %>%
paste0(url, .) %>%
read_csv(., skip = 2, col_types = cols())Load Protein Measurements spreadsheet exported from Progenesis.
Load Peptide Measurements spreadsheet exported from Progenesis.
pepm <- "data/20190123_EWM_AZD1_R_rank-lessthan11-include_uniprot_pepm.csv" %>%
paste0(url, .) %>%
read_csv(., skip = 2, col_types = cols())
pepm